SoroushVahidi/scidocs huggingface dataset:
Automated vehicle system architecture with performance assessment
(2017)

This paper proposes a reference architecture to increase reliability and robustness of an automated vehicle. The architecture exploits the benefits arising from the in-terdependencies of the system and provides self awareness. Performance Assessment units attached to subsystems quantify the reliability of their operation and return performance values. The Environment Condition Assessment, which is another important novelty of the architecture, informs augmented sensors on current sensing conditions. Utilizing environment conditions and performance values for subsequent centralized integrity checks allow algorithms to adapt to current driving conditions and thereby to increase their robustness. We demonstrate the benefit of the approach with the example of false positive object detection and tracking, where the detection of a ghost object is resolved in centralized performance assessment using a Bayesian network.
Using Intermediate Representations to Solve Math Word Problems
(2018)

To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system. However, such mappings do not include the information of a few higher-order operations that cannot be explicitly represented in equations but are required to solve the problem. The gap between natural language and equations makes it difficult for a learned model to generalize from limited data. In this work we present an intermediate meaning representation scheme that tries to reduce this gap. We use a sequence-to-sequence model with a novel attention regularization term to generate the intermediate forms, then execute them to obtain the final answers. Since the intermediate forms are latent, we propose an iterative labeling framework for learning by leveraging supervision signals from both equations and answers. Our experiments show using intermediate forms outperforms directly predicting equations.
Graph-based mining of multiple object usage patterns
(2009)

The interplay of multiple objects in object-oriented programming often follows specific protocols, for example certain orders of method calls and/or control structure constraints among them that are parts of the intended object usages. Unfortunately, the information is not always documented. That creates long learning curve, and importantly, leads to subtle problems due to the misuse of objects.
 In this paper, we propose GrouMiner, a novel graph-based approach for mining the usage patterns of one or multiple objects. GrouMiner approach includes a graph-based representation for multiple object usages, a pattern mining algorithm, and an anomaly detection technique that are efficient, accurate, and resilient to software changes. Our experiments on several real-world programs show that our prototype is able to find useful usage patterns with multiple objects and control structures, and to translate them into user-friendly code skeletons to assist developers in programming. It could also detect the usage anomalies that caused yet undiscovered defects and code smells in those programs.
Performance improvement of synchronous reluctance motors: A review
(2017)

Synchronous Reluctance motors can replace the more commonly used, induction, switched reluctance and permanent magnet motors. They have been shown to outperform similarly dimensioned induction motors in efficiency, torque and power density. They are simple and cheap to construct and require simple control. Such a motor would be ideal for wide spread use in Africa, which currently faces a power deficit, considering that motors account for about 43% of global electric power consumption. The use of synchronous reluctance motors in Electric Vehicles may contribute to the production of more efficient, heavier load and longer range vehicles. This would accelerate adoption of Electric Vehicles and it would help wean the world off fossil fuels. The synchronous reluctance motor has disadvantages of high torque ripple and poor power factor. A radical redesign of the rotor geometry and simultaneous optimisation of the rotor and the stator may significantly improve its performance.
Vectorized total variation defined by weighted L infinity norm for utilizing inter channel dependency
(2012)

Vectorized total variation (VTV) is very successful convex regularizer to solve various color image recovery problems. Despite the fact that color channels of natural color images are closely related, existing variants of VTV can not utilize this prior efficiently. We proposed L∞-VTV as a convex regularizer can penalize the violation of such inter-channel dependency by employing weighted L∞ (L-infty) norm. We also introduce an effective algorithm for an image denoising problem using L∞-VTV. Experimental results shows that our proposed method can outperform the conventional methods.
Crafting Integrated Multichannel Retailing Strategies
(2010)

Multichannel retailing is the set of activities involved in selling merchandise or services to consumers through more than one channel. Multichannel retailers dominate today's retail landscape. While there are many benefits of operating multiple channels, these retailers also face many challenges. In this article, we discuss the key issues concerning multichannel retailing, including the motivations and constraints of going multichannel, the challenges of crafting multichannel retailing strategies and opportunities for creating synergies across channels, key retail mix decisions facing multichannel retailers, and the dynamics of multichannel retailing. We synthesize current knowledge drawn from the academic literature and industry practice, and discuss potential directions for future research. © 2010 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.
Wire Speed Name Lookup: A GPU-based Approach
(2013)

This paper studies the name lookup issue with longest prefix matching, which is widely used in URL filtering, content routing/switching, etc. Recently Content-Centric Networking (CCN) has been proposed as a clean slate future Internet architecture to naturally fit the contentcentric property of today’s Internet usage: instead of addressing end hosts, the Internet should operate based on the identity/name of contents. A core challenge and enabling technique in implementing CCN is exactly to perform name lookup for packet forwarding at wire speed. In CCN, routing tables can be orders of magnitude larger than current IP routing tables, and content names are much longer and more complex than IP addresses. In pursuit of conquering this challenge, we conduct an implementation-based case study on wire speed name lookup, exploiting GPU’s massive parallel processing power. Extensive experiments demonstrate that our GPU-based name lookup engine can achieve 63.52M searches per second lookup throughput on large-scale name tables containing millions of name entries with a strict constraint of no more than the telecommunication level 100μs per-packet lookup latency. Our solution can be applied to contexts beyond CCN, such as search engines, content filtering, and intrusion prevention/detection. c ⃝Prof. Qunfeng Dong (qunfeng.dong@gmail.com) and Prof. Bin Liu (lmyujie@gmail.com), placed in alphabetic order, are the correspondence authors of the paper. Yi Wang and Yuan Zu, placed in alphabetic order, are the lead student authors of Tsinghua University and University of Science and Technology of China, respectively. This paper is supported by 863 project (2013AA013502), NSFC (61073171, 61073184), Tsinghua University Initiative Scientific Research Program(20121080068), the Specialized Research Fund for the Doctoral Program of Higher Education of China(20100002110051), the Ministry of Education (MOE) Program for New Century Excellent Talents (NCET) in University, the Science and Technological Fund of Anhui Province for Outstanding Youth (10040606Y05), by the Fundamental Research Funds for the Central Universities (WK0110000007, WK0110000019), and Jiangsu Provincial Science Foundation (BK2011360).
Enable IoT interoperability in ambient assisted living: Active and healthy aging scenarios
(2017)

Ambient Assisted Living (AAL) represents one of the most promising Internet of Things (IoT) applications due to its influence on the quality of life and health of the elderly people. However, the interoperability is one of the major issues that needs to be addressed to promote the adoption of AAL solutions in real environments. In this paper, an IoT prototype system for AAL called AAL-IoTSys is proposed, which includes a Smart IoT Gateway as a key component to enable the interoperability from several heterogeneous devices over different communication protocols and technologies (e.g., WiFi, ZigBee, Bluetooth, IEEE 802.15.4, 6LowPAN). This system aims to monitor environmental (temperature, humidity, C02), health (heart rate) and location (GPS) parameters in the elderly people's houses in order to facilitate events early detection and automatic sending notifications to elderly care stakeholders in active and healthy aging scenarios (included health professionals, caregivers, family members and emergency services).
Intelligent Tutoring Systems with Multiple Representations and Self-Explanation Prompts Support Learning of Fractions
(2009)

Although a solid understanding of fractions is foundational in mathematics, the concept of fractions remains a challenging one. Previous research suggests that multiple graphical representations (MGRs) may promote learning of fractions. Specifically, we hypothesized that providing students with MGRs of fractions, in addition to the conventional symbolic notation, leads to better learning outcomes as compared to instruction incorporating only one graphical representation. We anticipated, however, that MGRs would make the students’ task more challenging, since they must link the representations and distill from them a common concept or principle. Therefore, we hypothesized further that selfexplanation prompts would help students benefit from working with MGRs. To investigate these hypotheses, we conducted a classroom study in which 112 6grade students used intelligent tutors for fraction conversion and fraction addition. The results of the study show that students learned more with MGRs of fractions than with a single representation, but only when prompted to self-explain how the graphics relate to the symbolic fraction representations.
Boosted key-frame selection and correlated pyramidal motion-feature representation for human action recognition
(2013)

In this paper we propose a novel method for human action recognition based on boosted key-frame selection and correlated pyramidal motion feature representations. Instead of using an unsupervised method to detect interest points, a Pyramidal Motion Feature (PMF), which combines optical flow with a biologically inspired feature, is extracted from each frame of a video sequence. The AdaBoost learning algorithm is then applied to select the most discriminative frames from a large feature pool. In this way, we obtain the top-ranked boosted frames of each video sequence as the key frames which carry the most representative motion information. Furthermore, we utilise the correlogram which focuses not only on probabilistic distributions within one frame but also on the temporal relationships of the action sequence. In the classification phase, a Support-Vector Machine (SVM) is adopted as the final classifier for human action recognition. To demonstrate generalizability, our method has been systematically tested on a variety of datasets and shown to be more effective and accurate for action recognition compared to the previous work. We obtain overall accuracies of: 95.5%, 93.7%, and 36.5% with our proposed method on the KTH, the multiview IXMAS and the challenging HMDB51 datasets, respectively. & 2012 Elsevier Ltd. All rights reserved.
Consumers ’ attitude towards online shopping : Factors influencing employees of crazy domains to shop online
(2014)

E-commerce offers many online marketing opportunities to companies worldwide and along with high rapid growth of online shopping; it has impressed many retailers to sell products and services through online channel to expand their market. Online shopping or marketing is the use of technology (i.e., computer, internet) for better marketing performance. And retailers are mixing strategies to meet the demand of online shoppers; they are busy in studying consumer in the field of online shopping, to see the consumer attitudes towards online shopping and specifically studying the factors influencing consumers to shop online. In this study, the multiple regression analysis was employed to measure the relationship between 9 independent variables and receptivity to online shopping. The score of Beta weight presented that all 9 independent variables had positive statistical significant effect to Internet users to accept online shopping. Among the 9 factors, the strongest influencers from highest to lowest were Price, Refund, Convenience, Auction websites, Security, Brand, Search engines, Promotion and Online shopping malls. According to independent t-test analysis for gender, there was significant different means between males and females for online shopping malls and Auctions websites factors to receptivity on online shopping. The means of female significant higher than male for these two factors. This study might contribute not only to a better understanding on what and how strongly the factors are involved in online consumer purchasing decisions but also this study provides e-retailer’s standpoint such the effectively manage and recommendations. However, eretailers should keep in mind that consumer behavior might change in time to time especially in online market so the e-retailer should investigate the consumer behavior in time to time and adapt the products and services to serve as the customer requirements.
Big Data Mobile Services for New York City Taxi Riders and Drivers
(2016)

In recent years, taxis in New York City have been equipped with GPS sensors to record the start and end locations of every trip, in conjunction with fare collection equipment recording trip time, fare, and distance. This paper analyzes this vast dataset with big data technologies. We use MapReduce and Hive in order to understand traffic and travel patterns, discover relationships among the data, and make predictions on the taxi network. We also propose a service model combining the results of our data analysis with smartphone applications to offer users various taxi-related services based on results of our big data analysis.
Monogenic polyarteritis: the lesson of ADA2 deficiency
(2016)

The deficiency of Adenosine Deaminase 2 (DADA2) is a new autoinflammatory disease characterised by an early onset vasculopathy with livedoid skin rash associated with systemic manifestations, CNS involvement and mild immunodeficiency.This condition is secondary to autosomal recessive mutations of CECR1 (Cat Eye Syndrome Chromosome Region 1) gene, mapped to chromosome 22q11.1, that encodes for the enzymatic protein adenosine deaminase 2 (ADA2). By now 19 different mutations in CECR1 gene have been detected.The pathogenetic mechanism of DADA2 is still unclear. ADA2 in a secreted protein mainly expressed by cells of the myeloid lineage; its enzymatic activity is higher in conditions of hypoxia, inflammation and oncogenesis. Moreover ADA2 is able to induce macrophages proliferation and differentiation; it's deficiency is in fact associated with a reduction of anti-inflammatory macrophages (M2). The deficiency of ADA2 is also associated with an up-regulation of neutrophils-expressed genes and an increased secretion of pro-inflammatory cytokines. The mild immunodeficiency detected in many DADA2 patients suggests a role of this protein in the adaptive immune response; an increased mortality of B cells and a reduction in the number of memory B cells, terminally differentiated B cells and plasmacells has been described in many patients. The lack of the protein is associated with endothelium damage; however the function of this protein in the endothelial homeostasis is still unknown.From the clinical point of view, this disease is characterized by a wide spectrum of severity. Chronic or recurrent systemic inflammation with fever, elevation of acute phase reactants and skin manifestations (mainly represented by livedo reticularis) is the typical clinical picture. While in some patients the disease is mild and skin-limited, others present a severe, even lethal, disease with multi-organ involvement; the CNS involvement is rather common with ischemic or hemorrhagic strokes. In many patients not only the clinical picture but also the histopathologic features are undistinguishable from those of systemic polyarteritis nodosa (PAN). Of note, patients with an unusual phenotype, mainly dominated by clinical manifestations suggestive for an immune-disrective condition, have been described.Due to its rarity, the response to treatment of DADA2 is still anecdotal. While steroids can control the disease's manifestations at high dosage, none of the common immunosuppressive drugs turned out to be effective. Biologic drugs have been used only in few patients, without a clear effectiveness; anti-TNF drugs are those associated to a better clinical response. Hematopoietic stem cells transplantation was effective in patients with a severe phenotype.
Understanding and Modeling of WiFi Signal Based Human Activity Recognition
(2015)

Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.
Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences
(2003)

Opinion question answering is a challenging task for natural language processing. In this paper, we discuss a necessary component for an opinion question answering system: separating opinions from fact, at both the document and sentence level. We present a Bayesian classifier for discriminating between documents with a preponderance of opinions such as editorials from regular news stories, and describe three unsupervised, statistical techniques for the significantly harder task of detecting opinions at the sentence level. We also present a first model for classifying opinion sentences as positive or negative in terms of the main perspective being expressed in the opinion. Results from a large collection of news stories and a human evaluation of 400 sentences are reported, indicating that we achieve very high performance in document classification (upwards of 97% precision and recall), and respectable performance in detecting opinions and classifying them at the sentence level as positive, negative, or neutral (up to 91% accuracy).
Development and Use of a Gold-Standard Data Set for Subjectivity Classifications
(1999)

This paper presents a case study of analyzing and improving intercoder reliability in discourse tagging using statistical techniques. Bias-corrected tags are formulated and successfully used to guide a revision of the coding manual and develop an automatic classifier. 1 Introduction This paper presents a case study of analyzing and improving intercoder reliability in discourse tagging using the statistical techniques presented in (Bruce and Wiebe, 1998; Bruce and Wiebe, to appear). Our approach is data driven: we refine our understanding and presentation of the classification scheme guided by the results of the intercoder analysis. We also present the results of a probabilistic classifier developed on the resulting annotations. Much research in discourse processing has focused on task-oriented and instructional dialogs. The task addressed here comes to the fore in other genres, especially news reporting. The task is to distinguish sentences used to objectively present factual information from sentences used to present opinions and evaluations. There are many applications for which this distinction promises to be important, including text categorization and summarization. This research takes a large step toward developing a reliably annotated gold standard to support experimenting with such applications. This research is also a case study of analyzing and improving manual tagging that is applicable to any tagging task. We perform a statistical analysis that provides information that complements the information provided by
Inferring 3D layout of building facades from a single image
(2015)

In this paper, we propose a novel algorithm that infers the 3D layout of building facades from a single 2D image of an urban scene. Different from existing methods that only yield coarse orientation labels or qualitative block approximations, our algorithm quantitatively reconstructs building facades in 3D space using a set of planes mutually related by 3D geometric constraints. Each plane is characterized by a continuous orientation vector and a depth distribution. An optimal solution is reached through inter-planar interactions. Due to the quantitative and plane-based nature of our geometric reasoning, our model is more expressive and informative than existing approaches. Experiments show that our method compares competitively with the state of the art on both 2D and 3D measures, while yielding a richer interpretation of the 3D scene behind the image.
Suicide rates among physicians: a quantitative and gender assessment (meta-analysis).
(2004)

OBJECTIVE
Physicians' suicide rates have repeatedly been reported to be higher than those of the general population or other academics, but uncertainty remains. In this study, physicians' suicide rate ratios were estimated with a meta-analysis and systematic quality assessment of recent studies.


METHOD
Studies of physicians' suicide rates were located in MEDLINE, PsycINFO, AARP Ageline, and the EBM Reviews: Cochrane Database of Systematic Reviews with the terms "physicians," "doctors," "suicide," and "mortality." Studies were included if they were published in or after 1960 and gave estimates of age-standardized suicide rates of physicians and their reference population or reported extractable data on physicians' suicide; 25 studies met the criteria. Reviewers extracted data and scored each study for quality. The studies were tested for heterogeneity and publication bias and were stratified by publication year, follow-up, and study quality. Effect sizes were pooled by using fixed-effects (women) and random-effects (men) models.


RESULTS
The aggregate suicide rate ratio for male physicians, compared to the general population, was 1.41, with a 95% confidence interval (CI) of 1.21-1.65. For female physicians the ratio was 2.27 (95% CI=1.90-2.73). Visual inspection of funnel plots from tests of publication bias revealed randomness for men but some indication of bias for women, with a relative, nonsignificant lack of studies in the lower right quadrant.


CONCLUSIONS
Studies on physicians' suicide collectively show modestly (men) to highly (women) elevated suicide rate ratios. Larger studies should help clarify whether female physicians' suicide rate is truly elevated or can be explained by publication bias.